在住院登革热患者中,对任何血液成分输血的机器学习预测模型
Md Shahid Ansari1, Dinesh Jain1, Sandeep Budhiraja2
1Department of Clinical Data Analytics, Max Super Speciality Hospital, New Delhi, India.
Hematology, transfusion and cell therapy
|November 23, 2023
概括
机器学习模型可以预测登革热患者的血液成分输血. XGBoost 模型表现出强的性能,有助于及时准备和患者选.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 流行病学 流行病学
背景情况:
- 血液成分输血对于严重的登革热和血栓塞缩症至关重要.
- 预测输血需求对于在登革热流行期间有效管理医疗保健至关重要.
研究的目的:
- 调查登革热患者输血血液成分的危险因素.
- 评估用于预测输血需求的机器学习算法.
主要方法:
- 使用回顾性医院数据开发了八种预测模型.
- 使用SHAP (夏普利添加式扩展) 来实现模型的可解释性.
- 选择了16个重要的变量用于预测.
主要成果:
- 该XGBoost模型显示出优异的预测性能 (AUC:0.793).
- 确定了16个关键变量作为重要的预测因素.
- XGBoost的表现优于其他评估的机器学习模型.
结论:
- 机器学习可以优化血液成分的准备和患者的分类.
- 预测模型帮助医疗保健提供者做出明智的输血决策.
- 多层算法显示出预测登革热患者输血需求的潜力.
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